The Irish Pharmaceutical Industry over the Boom \nPeriod and Beyond (NIRSA) Working Paper Series. No. 39
Bibliographic record
Abstract
The pharmaceutical industry has been one of the strongest performing sectors of the \nCeltic Tiger era. During the past two decades, employment growth in the sector has \nbeen strong and continuous, even when, in recent years, employment in other \nmanufacturing sectors has been contracting. Although positive in itself, from a \ndynamic regional development perspective it is important to explore the qualitative \nchanges in the types of activities that are conducted in Ireland. Adopting a global \nproduction network approach, the paper examined Ireland’s changing role in global \nproduction networks within the pharmaceutical industry, focussing on the different \ncomponents of manufacturing and R&D. The analysis shows that Ireland’s \ninvolvement in manufacturing has shifted in the direction of relatively higher value \ngenerating activities. Within R&D, although the level of value creation has increased \nsubstantially, Ireland’s involvement remains concentrated in the (relatively) lower \nvalue generating activities of the global R&D network. In addition, the sector remains \nstrongly dominated by foreign direct investment so that a large share of the created \nvalue is not captured within Ireland.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".